Instructions to use djrsystemservices/Qwopus3-5-9B-Coder-Flash-oQ4e-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use djrsystemservices/Qwopus3-5-9B-Coder-Flash-oQ4e-mtp with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download djrsystemservices/Qwopus3-5-9B-Coder-Flash-oQ4e-mtp --local-dir Qwopus3-5-9B-Coder-Flash-oQ4e-mtp
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Qwopus3-5-9B-Coder-Flash-oQ4e-mtp
This is an oQ4e MLX conversion produced with oMLX.
Source and credit
- Original local source checkpoint:
Qwopus3-5-9B-Coder-Flash-bf16 - Source repository / credit: https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash
Please follow the original model's license, acceptable-use policy, and attribution requirements.
Conversion
- Quantization: oQ4e (
enhanced=True) - Importance calibration: imatrix, 128 samples × 512 tokens
- oMLX version: 0.6.3rc2
oQe uses an imatrix sensitivity calibration to assign precision selectively,
rather than applying a uniform bit-width to every tensor. The generated
oq_imatrix_report.json records the resulting quantization allocation.
python run_oqe_conversion.py Qwopus3-5-9B-Coder-Flash-bf16 --models-dir /path/to/models --oq-level 4.0 --imatrix-samples 128 --imatrix-seq-length 512 --source-repo https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash
Files
model.safetensorsormodel-*-of-*.safetensors: quantized MLX weightsmodel.safetensors.index.json: shard index (present for sharded outputs)config.json: model configurationoq_imatrix_report.json: imatrix calibration and quantization report
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Model size
10B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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4-bit
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